# Interpreting coefficients of ordinal logistic regression when there is clustering within the data

I have built and refined a regression model using the ordinal package in R. The measure is $0>1>2>3>4>5$ (Yes/No questions) and is repeated every 10 minutes for an hour (episode) within the same person, twice a week for up to 6 weeks, but average 3 weeks. I have 6000 such observations on 1000 hours for 150 people.

When I built a univariate model using number of questions $(0-5)$ answered as the dependent variable and say, Gender as the independent variable, with random intercepts for person and episode I interpreted the coefficients as "Difference in the proportional log-odds of one extra question being answered on average over all timepoints".

My question is: Is that valid, or have I made unsupportable assumptions due to the nature of the mixed effects model for which I am not accounting.

Incidentally I can completely recommend the "ordinal" package, although it is in development, and has minor difficulties with convergence for some unstandardised parameters.

Edit: I've only given commentary on interpretation of your point estimates. One should note that if you've just used the $p$-values produced from an ordinary ordinal logistic regression model, then your inference may be biased. You may want to consider using a bootstrap procedure or deriving robust Huber-White sandwich standard errors for your model to get approximately unbiased confidence intervals and $p$-values.